In recent years, techniques for tracking and detecting moving objects, such as human beings and vehicles, have been developing in the field of video monitoring. A large number of methods for detecting and tracking moving objects have been proposed.
One method for detecting a moving object includes a background subtraction method. This method compares an image captured by a camera with an image previously stored as a background and extracts an area representing a difference between two images as a moving object.
Two methods for extracting a background include a method for extracting a background by using data obtained at an observation start time and a method for extracting a background by calculating the average of observation results in a most recent certain time period. The former method for extracting a background by using data obtained at an observation start time may not keep pace with a background that varies continually. Therefore, the latter method for extracting a background by using several observation results may be typically adopted.
There also may be a method for extracting a temporarily resting object, such as an object left behind or a human being staying in a certain time period. The method may analyze motion in a scene using background models obtained over a plurality of time periods. In the method, a long-period background model, which is obtained by analyzing background images observed over a long time period, and a short-period background model, which is obtained by analyzing background images observed over a short time period, may be created. If a moving object is not detected by a background subtraction method based on the short-period background model, but a moving object is detected by a background subtraction method based on the long-period background model, the method may determine that the moving object is temporarily resting.
In other aspects, a moving object tracking method may use a Kalman filter or a particle filter. This method may assume that a target object to be tracked moves at a constant velocity or at a constant acceleration.
In another aspect, a moving object tracking device may continue tracking a moving object even if the moving object temporarily stands still at the same position where a background object exists. In other words, the moving object tracking device may track a stationary object as well as a moving object in a similar fashion and may determine whether an object being tracked is a stationary object or a moving object on the basis of the history of detection information about the object.
In a tracking method using a Kalman filter or a particle filter, it may be assumed that the motion of a tracking target is a comparatively simple motion such as constant velocity linear motion or constant acceleration motion. However, the motion of a moving object such as a human being or a vehicle may not be so simple, and there may be a case where the moving object is in a moving state or temporarily in a resting state.
For example, there may be a case where a human being and a vehicle temporarily stop at the place where there is a traffic light. Further, in the case where a moving object is a human being, if he/she is waiting for someone else, he/she may temporarily stop his/her motion. Similarly, if she/he is in a shop, he/she may temporarily stop to check out particular goods. Further, a suspicious individual may temporarily stop to watch surrounding circumstances. Further, there may be a case where a vehicle temporarily stops in a parking lot.
As described above, there may be many moving objects that repeated move and stop. In a typical tracking method, it may be assumed that the movement of a moving object is simple. Therefore, a typical tracking method may fail to detect a moving object if the moving object repeatedly moves and stops.